A library of quantitative markers of seizure severity

Gascoigne, Sarah J; Waldmann, Leonard; Schroeder, Gabrielle M; Panagiotopoulou, Mariella; Jess, Blickwedel,; Chowdhury, Fahmida A; Cronie, Alison; Diehl, Beate; Duncan, John; Jennifer, Falconer,; Faulder, Ryan; Yu, Guan,; Veronica, Leach,; Shona, Livingstone,; Papasavvas, Christoforos; Thomas, Rhys H.; Wilson, Kevin J.; Taylor, Peter Neal; Wang, Yujiang

doi:10.1111/epi.17525

Cited by 7 publications

(5 citation statements)

References 46 publications

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“…These temporal fluctuations could reveal different pathological subnetworks and relate to seizure occurrence, 21 for example, when they coalesce in space and time 22–24 . It is also possible that they are related to seizure severity or other temporally modulated properties of seizures 9,25,26 . The notion of fluctuating pathological subnetworks may also suggest alternative treatment strategies of network resections, disconnections, or closed‐loop neuromodulation of subnetworks 22–24 .…”

Section: Discussionmentioning

confidence: 99%

“… 22 , 23 , 24 It is also possible that they are related to seizure severity or other temporally modulated properties of seizures. 9 , 25 , 26 The notion of fluctuating pathological subnetworks may also suggest alternative treatment strategies of network resections, disconnections, or closed‐loop neuromodulation of subnetworks. 22 , 23 , 24 Finally, these abnormalities may not be epileptogenic per se, but relate to other temporally changing impairments in, for example, mood or cognitive performance.…”

Section: Discussionmentioning

confidence: 99%

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Temporal stability of intracranial electroencephalographic abnormality maps for localizing epileptogenic tissue

et al. 2023

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ObjectiveIdentifying abnormalities on interictal intracranial electroencephalogram (iEEG), by comparing patient data to a normative map, has shown promise for the localization of epileptogenic tissue and prediction of outcome. The approach typically uses short interictal segments of approximately 1 min. However, the temporal stability of findings has not been established.MethodsHere, we generated a normative map of iEEG in nonpathological brain tissue from 249 patients. We computed regional band power abnormalities in a separate cohort of 39 patients for the duration of their monitoring period (.92–8.62 days of iEEG data, mean = 4.58 days per patient, >4800 hours recording). To assess the localizing value of band power abnormality, we computed —a measure of how different the surgically resected and spared tissue was in terms of band power abnormalities—over time.ResultsIn each patient, the value was relatively consistent over time. The median of the entire recording period separated seizure‐free (International League Against Epilepsy [ILAE] = 1) and not‐seizure‐free (ILAE 1) patients well (area under the curve [AUC] = .69). This effect was similar interictally (AUC = .69) and peri‐ictally (AUC = .71).SignificanceOur results suggest that band power abnormality D_RS, as a predictor of outcomes from epilepsy surgery, is a relatively robust metric over time. These findings add further support for abnormality mapping of neurophysiology data during presurgical evaluation.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Temporal stability of intracranial electroencephalographic abnormality maps for localizing epileptogenic tissue

et al. 2023

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“…We also tested Support Vector Machine and Linear Discriminant Analysis classifiers, but both provided poorer classification. Our approach of using a range of simple-to-complex features is different from older localisation methods which used one main feature such as high-to-low frequency energy ratio 13,16 , and aligns with later studies which have combined several features for EZ localisation 17,31,61,62 , temporal detection of seizures 63 and quantification of seizure severity 64 .…”

Section: Discussionmentioning

confidence: 99%

Generalisability of epileptiform patterns across time and patients

Karimi-Rouzbahani,

McGonigal

2023

Preprint

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Complexity of epileptogenic zone (EZ) localisation contributes to failure of surgical resection to produce seizure freedom. This is to some extent a result of distinct patterns of epileptiform activity between (i.e., interictal) and during seizures (i.e., ictal) and their diversity across patients. This often leads to suboptimal localisation based on inspection of electroencephalography (EEG) features. We asked two under-investigated questions. First, whether neural signal reflecting epileptogenicity would be generalisable from interictal to ictal time window within each patient. This would be critical for patients who are monitored in hospital without having a seizure to help with EZ localisation, and more generally for understanding the predictive power of resting state (interictal) EEG data in determining EZ. Second, whether epileptiform patterns would generalise across patients, and if so, which aspects of those patterns are the most generalisable. We used an intracranial EEG dataset that included fifty-five patients with lesional and non-lesional pathology, who had subsequently undergone cortical resection in frontal or temporal lobe with different levels of seizure freedom. We extracted a large set of simple to complex features from stereo-EEG (SEEG) and electrocorticographic (ECoG) neural signals recorded during interictal and ictal time windows. We fed those features to decision-tree classifiers for EZ localisation and to quantify the diversity of ictal and interictal epileptiform patterns through a cross-time and cross-patient generalisation procedure. We observed significant evidence (Bayes factor >> 10) for generalisability of patterns from interictal to ictal time windows across patients, which were dominantly reflected in signal power and high-frequency network-based features. Majority of patients showed consistent patterns of epileptogenicity across interictal and ictal time windows, reflected in above-chance area-under-curve (mean AUC = 0.6). We observed significant evidence (Bayes factor >> 10) that signal features of epileptogenic regions could generalise across patients in both interictal and ictal time windows with significant evidence for higher generalisability in ictal than interictal time window (mean AUC 0.75 vs. 0.59; Bayes factor >> 10). While signal power and moment features were the most contributory to the cross-patient generalisation in the interictal window, signal complexity features were the most contributory in the ictal window. These results provide new insights about features of epileptic neural activity that generalise across interictal-ictal time windows and patients, which can have implications for both qualitative and quantitative EZ localisation. The explainable machine-learning pipeline developed here can guide future developments in epilepsy investigations.

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“…However, other studies have related seizure networks to clinical seizure types. 9 , 20-22 Thus, some SNSs likely relate to seizure clinical characteristics, and future studies could use clinical seizure data or quantitative seizure severity markers 62 to explore this relationship and investigate how seizure symptoms fluctuate over time. Finally, other datasets, such as pre-surgical iEEG recordings, provide better spatial coverage as well as accompanying neuroimaging data.…”

Section: Discussionmentioning

confidence: 99%

Chronic intracranial EEG recordings and interictal spike rate reveal multiscale temporal modulations in seizure states

Schroeder

Karoly

Maturana

et al. 2023

Brain Communications

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Many biological processes are modulated by rhythms on circadian and multidien timescales. In focal epilepsy, various seizure features, such as spread and duration, can change from one seizure to the next within the same patient. However, the specific timescales of this variability, as well as the specific seizure characteristics that change over time, are unclear. Here, in a cross-sectional observational study, we analysed within-patient seizure variability in 10 patients with chronic intracranial EEG recordings (185-767 days of recording time, 57-452 analysed seizures/patient). We characterised the seizure evolutions as sequences of a finite number of patient-specific functional seizure network states. We then compared seizure network state occurrence and duration to (1) time since implantation and (2) patient-specific circadian and multidien cycles in interictal spike rate. In most patients, the occurrence or duration of at least one seizure network state was associated with the time since implantation. Some patients had one or more seizure network states that were associated with phases of circadian and/or multidien spike rate cycles. A given seizure network state’s occurrence and duration were usually not associated with the same timescale. Our results suggest that different time-varying factors modulate within-patient seizure evolutions over multiple timescales, with separate processes modulating a seizure network state’s occurrence and duration. These findings imply that the development of time-adaptive treatments in epilepsy must account for several separate properties of epileptic seizures, and similar principles likely apply to other neurological conditions.

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A library of quantitative markers of seizure severity

Cited by 7 publications

References 46 publications

Temporal stability of intracranial electroencephalographic abnormality maps for localizing epileptogenic tissue

Temporal stability of intracranial electroencephalographic abnormality maps for localizing epileptogenic tissue

Generalisability of epileptiform patterns across time and patients

Chronic intracranial EEG recordings and interictal spike rate reveal multiscale temporal modulations in seizure states

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